Sabisu Analytics

From performance metrics to deep learning with industrial and enterprise data.

Analytics with Sabisu

Meeting all asset, project and portfolio performance requirements

Sabisu combines deep learning and statistical techniques to provide state of the art monitoring and automatically initiate workflows.

All KPIs and metrics are automated using Sabisu Pipelines, aggregating raw data from enterprise and process sources to ensure accuracy and ad hoc analysis at all levels; process, plant, site, project and enterprise.

Key metrics such as time-on-tools are continuously maintained for instant comparison, analysis and optimisation – they’re up to date, all the time.

Project Analytics

Strengthening project controls & providing early warning

Asset Models are developed throughout the lifecycle to reflect progress and compared to reference models to predict outcome. This provides an ‘as built’ model which can be passed into operations through CSU.

Reference Models are used to inform early stage forecasts and scope optimisation by acting as a ‘solution library’, reducing engineering costs and linking 6D CAD, vendor and historical performance data to improve predictions.

Techniques & Technology

Sabisu has the technology needed real-time control of complex projects & operations

Sabisu uses cloud computing and Spark, a highly scalable and distributed in-memory MapReduce system which is exceptionally fast, wherever possible.

Analytics are executed on a micro-service architecture, ensuring performance at scale. High performance libraries including MXNET and SparkML are used for machine / deep learning.

Customers requiring on-premise deployment have options including integration with Sabisu Cloud Central Services through a secure RESTful web interface or VPN, or implementing a local analytics service.

Artificial intelligence techniques are used to perform sentiment analysis, extracting quantitative information from commentary, narrative & logs which then informs other analytics.

Python is used both in prototyping and production to ensure rapid deployment of custom analytics solutions.

Third party Python libraries are often provided for inclusion in the platform.